where \(j,k \in U\) . If we utilize Eq.(REF ) directly, some critical information may be ignored, which leads to learning inaccurate metric. In real-world recommender systems, to cluster different users, the different attribute should take different importance and arrange different weights, which is the theory of attention mechanism [1]}. The intuitive idea is that the attributes and sentiment gaps should make different contributions to metric learning. With this restriction, we can add the attention vector \(t\)  into Eq.(REF ), using \(t \otimes z\)  to replace \(z\) . Note that \(\otimes \)  means element-wise product.
